Voltage Prediction

Voltage prediction in power systems is crucial for grid stability and efficient operation, focusing on accurately forecasting voltage levels under various conditions, including contingencies and faults. Recent research emphasizes developing data-efficient and robust prediction models, employing techniques like graph neural networks (GNNs) leveraging power system topology, and physics-informed deep learning approaches that integrate physical models with neural networks to improve accuracy and transferability across different grid configurations. These advancements aim to enhance the speed, accuracy, and scalability of voltage predictions, leading to improved grid management and control strategies.